ML Engineering & Deep Learning
Applied ML and theory
One ML system that operators run every day, and the theory that tells you how hard you can push a training run. Both come with numbers you can check. Open a project for the full case study.
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Forecasting · deployed
Telecom sites in Cameroon were losing diesel and nobody could see where. This predicts what each site should burn and flags the ones burning more. Operators still run it.
NSE 0.986 · 84,617 liters of fuel accounted for
A Random Forest regressor behind a Flask web app, with a deviation threshold at the mean plus 2 standard deviations, a monitoring dashboard, and exports for audits.
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Doctoral research · published
Analysis of training neural networks
Springer Nature 2024How large a training step you can take before a model stops learning, worked out in advance instead of found by watching a run fail.
The maximum learning rate stops decaying exponentially with depth
Global convergence of gradient descent for deep linear networks, carrying earlier gradient flow analyses into the discrete setting and proving convergence to a global minimum for almost all initializations.
